AI Solution Architect/Tech Lead

We are seeking an experienced AI Technical Lead AI Solution Architect to design and deliver production-grade AI ML solutions within a government or highly regulated environment The role combines hands-on AI engineering solution architecture Generative AI expertise and technical leadership The successful candidate will define technical solutions guide implementation review architecture and code and technically lead AI delivery teamsKey RequirementsProven hands-on experience designing and delivering AI ML solutions into production Strong practical knowledge of Machine Learning Generative AI LLMs and Agentic AI Strong Python development skills with the ability to prototype review and guide production AI ML implementations Practical experience with RAG embeddings vector search prompt engineering tool function calling agent orchestration and LLM evaluation Strong AI solution architecture and integration capabilities including scalability security reliability performance and cost considerations Experience designing AI solutions across public cloud hybrid private or sovereign cloud environments Strong Microsoft Azure experience ideally including Azure OpenAI Azure AI Foundry Azure AI Search and Azure Machine Learning Understanding of MLOps LLMOps including deployment evaluation monitoring versioning and production lifecycle management Strong understanding of AI security governance responsible AI data privacy and data sovereignty Experience with modern software engineering practices including APIs microservices containers Kubernetes and CI CD Proven experience technically leading and mentoring AI Developers ML Engineers and Data Scientists strong Technical Stack strong Experience with relevant technologies across GenAI LLM Azure OpenAI Azure AI Foundry OpenAI-compatible APIs Hugging Face or equivalent RAG amp Search Azure AI Search vector databases embeddings and semantic search Agentic AI LangChain LangGraph Semantic Kernel LlamaIndex or equivalent agent frameworks ML MLOps Azure Machine Learning MLflow or equivalent Cloud amp Engineering Microsoft Azure REST APIs microservices Docker Kubernetes Git and CI CD Data SQL NoSQL data pipelines data lakes lakehouses Databricks Spark or Microsoft Fabric are advantageous Depth of technical capability is more important than experience with every named technology strong Preferred Experience strong Government or highly regulated environments Experience with data residency sovereignty security and regulatory requirements AI governance cybersecurity and responsible AI Production MLOps LLMOps Computer Vision High-volume scalable or real-time AI solutions Open-source or self-hosted LLMs Hybrid private or sovereign cloud architectures Proven hands-on experience designing and delivering AI/ML solutions into production Strong practical knowledge of Machine Learning Generative AI/LLMs and Agentic AI Strong Python development skills with the ability to prototype review and guide production AI/ML implementations Practical experience with RAG embeddings vector search prompt engineering tool/function calling agent orchestration and LLM evaluation Strong AI solution architecture and integration capabilities including scalability security reliability performance and cost considerations Experience designing AI solutions across public cloud hybrid private or sovereign cloud environments Strong Microsoft Azure experience ideally including Azure OpenAI Azure AI Foundry Azure AI Search and Azure Machine Learning Understanding of MLOps/LLMOps including deployment evaluation monitoring versioning and production lifecycle management Strong understanding of AI security governance responsible AI data privacy and data sovereignty Experience with modern software engineering practices including APIs microservices containers Kubernetes and CI/CD Proven experience technically leading and mentoring AI Developers ML Engineers and Data Scientists Government or highly regulated environments Experience with data residency sovereignty security and regulatory requirements AI governance cybersecurity and responsible AI Production MLOps / LLMOps Computer Vision High-volume scalable or real-time AI solutions Open-source or self-hosted LLMs Hybrid, private or sovereign cloud architectures